Extrem(ist) Fightin’ Words

Associated Press 1994-2010: Keyword:“Extremist”

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Load Libraries

### https://cran.r-project.org/web/packages/udpipe/vignettes/udpipe-usecase-postagging-lemmatisation.html
library(udpipe)
ud_model <- udpipe_download_model(language = "english")

library(tidyverse)
library(tidyr)
library(dplyr)
library(ggplot2)
library(ggrepel)
library(knitr)
library(tm)
library(quanteda)
library(lattice)
library(latticeExtra)
library(plotly)
library(pdp)
library(patchwork)
library(lubridate)

Load the FW functions

### CODE DIRECTLY FROM: https://burtmonroe.github.io/TextAsDataCourse/Tutorials/TADA-FightinWords.nb.html#
fwgroups <- function(dtm, groups, pair = NULL, weights = rep(1,nrow(dtm)), k.prior = .1) {
  
  weights[is.na(weights)] <- 0
  
  weights <- weights/mean(weights)
  
  zero.doc <- rowSums(dtm)==0 | weights==0
  zero.term <- colSums(dtm[!zero.doc,])==0
  
  dtm.nz <- apply(dtm[!zero.doc,!zero.term],2,"*", weights[!zero.doc])
  
  g.prior <- tcrossprod(rowSums(dtm.nz),colSums(dtm.nz))/sum(dtm.nz)
  
  # 
  
  g.posterior <- as.matrix(dtm.nz + k.prior*g.prior)
  
  groups <- groups[!zero.doc]
  groups <- droplevels(groups)
  
  g.adtm <- as.matrix(aggregate(x=g.posterior,by=list(groups=groups),FUN=sum)[,-1])
  rownames(g.adtm) <- levels(groups)
  
  g.ladtm <- log(g.adtm)
  
  g.delta <- t(scale( t(scale(g.ladtm, center=T, scale=F)), center=T, scale=F))
  
  g.adtm_w <- -sweep(g.adtm,1,rowSums(g.adtm)) # terms not w spoken by k
  g.adtm_k <- -sweep(g.adtm,2,colSums(g.adtm)) # w spoken by groups other than k
  g.adtm_kw <- sum(g.adtm) - g.adtm_w - g.adtm_k - g.adtm # total terms not w or k 
  
  g.se <- sqrt(1/g.adtm + 1/g.adtm_w + 1/g.adtm_k + 1/g.adtm_kw)
  
  g.zeta <- g.delta/g.se
  
  g.counts <- as.matrix(aggregate(x=dtm.nz, by = list(groups=groups), FUN=sum)[,-1])
  
  if (!is.null(pair)) {
    pr.delta <- t(scale( t(scale(g.ladtm[pair,], center = T, scale =F)), center=T, scale=F))
    pr.adtm_w <- -sweep(g.adtm[pair,],1,rowSums(g.adtm[pair,]))
    pr.adtm_k <- -sweep(g.adtm[pair,],2,colSums(g.adtm[pair,])) # w spoken by groups other than k
    pr.adtm_kw <- sum(g.adtm[pair,]) - pr.adtm_w - pr.adtm_k - g.adtm[pair,] # total terms not w or k
    pr.se <- sqrt(1/g.adtm[pair,] + 1/pr.adtm_w + 1/pr.adtm_k + 1/pr.adtm_kw)
    pr.zeta <- pr.delta/pr.se
    
    return(list(zeta=pr.zeta[1,], delta=pr.delta[1,],se=pr.se[1,], counts = colSums(dtm.nz), acounts = colSums(g.adtm)))
  } else {
    return(list(zeta=g.zeta,delta=g.delta,se=g.se,counts=g.counts,acounts=g.adtm))
  }
}

############## FIGHTIN' WORDS PLOTTING FUNCTION

# helper function
makeTransparent<-function(someColor, alpha=100)
{
  newColor<-col2rgb(someColor)
  apply(newColor, 2, function(curcoldata){rgb(red=curcoldata[1], green=curcoldata[2],
                                              blue=curcoldata[3],alpha=alpha, maxColorValue=255)})
}

fw.ggplot.groups <- function(fw.ch, groups.use = as.factor(rownames(fw.ch$zeta)), max.words = 50, max.countrank = 400, colorpalette=rep("black",length(groups.use)), sizescale=2, title="Comparison of Terms by Groups", subtitle = "", caption = "Group-specific terms are ordered by Fightin' Words statistic (Monroe, et al. 2008)") {
  if (is.null(dim(fw.ch$zeta))) {## two-group fw object consists of vectors, not matrices
    zetarankmat <- cbind(rank(-fw.ch$zeta),rank(fw.ch$zeta))
    colnames(zetarankmat) <- groups.use
    countrank <- rank(-(fw.ch$counts))
  } else {
    zetarankmat <- apply(-fw.ch$zeta[groups.use,],1,rank)
    countrank <- rank(-colSums(fw.ch$counts))
  }
  wideplotmat <- as_tibble(cbind(zetarankmat,countrank=countrank))
  wideplotmat$term=names(countrank)
  rankplot <- gather(wideplotmat, groups.use, zetarank, 1:ncol(zetarankmat))
  rankplot$plotsize <- sizescale*(50/(rankplot$zetarank))^(1/4)
  rankplot <- rankplot[rankplot$zetarank < max.words + 1 & rankplot$countrank<max.countrank+1,]
  rankplot$groups.use <- factor(rankplot$groups.use,levels=groups.use)
  
  p <- ggplot(rankplot, aes((nrow(rankplot)-countrank)^1, -(zetarank^1), colour=groups.use)) + 
    geom_point(show.legend=F,size=sizescale/2) + 
    theme_classic() +
    theme(axis.ticks=element_blank(), axis.text=element_blank() ) +
    ylim(-max.words,40) +
    facet_grid(groups.use ~ .) +
    geom_text_repel(aes(label = term), size = rankplot$plotsize, point.padding=.05,
                    box.padding = unit(0.20, "lines"), show.legend=F) +
    scale_colour_manual(values = alpha(colorpalette, .7)) + 
    labs(x=paste("Terms used more frequently overall -->"), y=paste("Terms used more frequently by group -->"),  title=title, subtitle=subtitle , caption = caption) 
  
}

fw.keys <- function(fw.ch,n.keys=10) {
  n.groups <- nrow(fw.ch$zeta)
  keys <- matrix("",n.keys,n.groups)
  colnames(keys) <- rownames(fw.ch$zeta)
  
  for (g in 1:n.groups) {
    keys[,g] <- names(sort(fw.ch$zeta[g,],dec=T)[1:n.keys])
  }
  keys
}

Compare Associated Press 1994-2010: Before and After “extremist”

Load and clean the data

• to string & lower text
• pivot to long format
• apply text_cleaner to one column “context.text”

######################################

### Text Cleaning Function

text_cleaner<-function(corpus){
  tempcorpus<-Corpus(VectorSource(corpus))
  tempcorpus<-tm_map(tempcorpus,
                    removePunctuation)
  tempcorpus<-tm_map(tempcorpus,
                    stripWhitespace)
  tempcorpus<-tm_map(tempcorpus,
                    removeNumbers)
  tempcorpus<-tm_map(tempcorpus,
                     removeWords, stopwords("english"))
  tempcorpus<-tm_map(tempcorpus, 
                    stemDocument)
  return(tempcorpus)
}

######################################

Calculate FW.

[1] 0.9900516

##################################################################

fw.extrem <- fwgroups(extrem_dtm, groups=extrem_NYT.dfm.long$Context)

Error: vector memory exhausted (limit reached?)

Get and show the top words per group by zeta.

##################################################################

fwkeys.extrem <- fw.keys(fw.extrem, n.keys=20)
cols <- rev(colnames(fwkeys.extrem))
fwkeys.extrem <- fwkeys.extrem[,cols]

##################################################################

kable(fwkeys.extrem)

Context.before	Context.after
muslim	group
jewish	organ
rightw	movement
islamic	respons
member	element
rrb	hutu
suspect	network
belong	ideolog
ban	militia
lrb	oppos
alqaidalink	view
small	activ
rabin	bomb
sunni	sayyaf
assassin	threaten
ap	parti
alleg	abu
outlaw	attack
yitzhak	alqaida
help	govern

##################################################################

Plot: Comparing Words Before (in Blue), and After (in Red)

p.fw.extrem <- fw.ggplot.groups(fw.extrem,sizescale=4,max.words=200,
                                max.countrank=400,colorpalette=c("red","blue"),
                                 title = 'Comparison of Terms Before and After "Extremist"')
p.fw.extrem

Calculate Parts of speech by before and after

##################################################################

ud_model <- udpipe_load_model(ud_model$file_model)

txt <-as.character(extrem_NYT.dfm.long$context.text)

x_udp <- udpipe_annotate(ud_model, x = txt, doc_id = seq_along(txt))
x <- as.data.frame(x_udp)

x$doc_id <-as.integer(x$doc_id)


##################################################################

##################################################################

x_odd.before <- x[x$doc_id %% 2 == 1,]
x_even.after <-x[x$doc_id %% 2 == 0, ]

##################################################################

Bar Charts for Parts of Speech

Cooccurences

Cooccurences (part 2)

Corrs(x_odd.before)

Corrs(x_even.after)

NA

Extrem(ist) Fightin’ Words Over Time

library(tweenr)

TransitionTime2 <- ggproto(
  "TransitionTime2",
  TransitionTime,
  expand_panel = function (self, data, type, id, match, ease, enter, exit, params, 
                           layer_index) {
    row_time <- self$get_row_vars(data)
    if (is.null(row_time)) 
      return(data)
    data$group <- paste0(row_time$before, row_time$after)
    time <- as.integer(row_time$time)
    states <- split(data, time)
    times <- as.integer(names(states))
    nframes <- diff(times)
    nframes[1] <- nframes[1] + 1
    if (times[1] <= 1) {
      all_frames <- states[[1]]
      states <- states[-1]
    }
    else {
      all_frames <- data[0, , drop = FALSE]
      nframes <- c(times[1] - 1, nframes)
    }
    if (times[length(times)] < params$nframes) {
      states <- c(states, list(data[0, , drop = FALSE]))
      nframes <- c(nframes, params$nframes - times[length(times)])
    }
    for (i in seq_along(states)) {
      all_frames <- switch(type, point = tween_state(all_frames, 
                                                     states[[i]], ease, nframes[i], 
                                                     !!id, enter, exit), 
                           path = transform_path(all_frames, 
                                                 states[[i]], ease, nframes[i], 
                                                 !!id, enter, exit, match), 
                           polygon = transform_polygon(all_frames, 
                                                       states[[i]], ease, nframes[i], 
                                                       !!id, enter, exit, match), 
                           sf = transform_sf(all_frames, 
                                             states[[i]], ease, nframes[i], 
                                             !!id, enter, exit), 
                           stop(type, 
                                " layers not currently supported by transition_time", 
                                call. = FALSE))
    }
    true_frame <- seq(times[1], times[length(times)])
    all_frames <- all_frames[
      all_frames$.frame %in% 
        # which(true_frame > 0 & true_frame <= params$nframes),
        true_frame[which(true_frame > 0 & true_frame <= params$nframes)], # tweak line A
      , 
      drop = FALSE]
    # all_frames$.frame <- all_frames$.frame - min(all_frames$.frame) + 1 # remove line B
    all_frames$group <- paste0(all_frames$group, "<", all_frames$.frame, ">")
    all_frames$.frame <- NULL
    all_frames
  })

transition_time2 <- function (time, range = NULL) {
  time_quo <- enquo(time)
  gganimate:::require_quo(time_quo, "time")
  ggproto(NULL, TransitionTime2, 
          params = list(time_quo = time_quo, range = range))
}

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Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!

<ggproto object: Class EaseAes, gg>
    aes_names: 
    aesthetics: 
    default: linear
    get_ease: function
    super:  <ggproto object: Class EaseAes, gg>

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